Abstract
Exposure to traumatic events is common. While many individuals recover following trauma exposure, a substantial subset develop adverse posttraumatic neuropsychiatric sequelae (APNS) such as posttraumatic stress, major depression, and regional or widespread chronic musculoskeletal pain. APNS cause substantial burden to the individual and to society, causing functional impairment and physical disability, risk for suicide, lost workdays, and increased health care costs. Contemporary treatment is limited by an inability to identify individuals at high risk of APNS in the immediate aftermath of trauma, and an inability to identify optimal treatments for individual patients. Our purpose is to provide a comprehensive review describing candidate blood-based biomarkers that may help to identify those at high risk of APNS and/or guide individual intervention decision-making. Such blood-based biomarkers include circulating biological factors such as hormones, proteins, immune molecules, neuropeptides, neurotransmitters, mRNA, and noncoding RNA expression signatures, while we do not review genetic and epigenetic biomarkers due to other recent reviews of this topic. The current state of the literature on circulating risk biomarkers of APNS is summarized, and key considerations and challenges for their discovery and translation are discussed. We also describe the AURORA study, a specific example of current scientific efforts to identify such circulating risk biomarkers and the largest study to date focused on identifying risk and prognostic factors in the aftermath of trauma exposure.
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References
Kilpatrick DG, Resnick HS, Milanak ME, Miller MW, Keyes KM, Friedman MJ. National estimates of exposure to traumatic events and PTSD prevalence using DSM‐IV and DSM‐5 criteria. J Trauma stress. 2013;26:537–47.
McLean SA, Ressler K, Koenen KC, Neylan T, Germine L, Jovanovic T, et al. The AURORA study: a longitudinal, multimodal library of brain biology and function after traumatic stress exposure. Mol Psychiatry. 2019. https://doi.org/10.1038/s41380-019-0581-3.
Gaskin DJ, Richard P. The economic costs of pain in the United States. J Pain. 2012;13:715–24.
Dobie DJ, Kivlahan DR, Maynard C, Bush KR, Davis TM, Bradley KA. Posttraumatic stress disorder in female veterans: association with self-reported health problems and functional impairment. Arch Intern Med. 2004;164:394–400.
Outcalt SD, Kroenke K, Krebs EE, Chumbler NR, Wu J, Yu Z, et al. Chronic pain and comorbid mental health conditions: independent associations of posttraumatic stress disorder and depression with pain, disability, and quality of life. J Behav Med. 2015;38:535–43.
Kessler RC. Posttraumatic stress disorder: the burden to the individual and to society. J Clin Psychiatry. 2000;61:4–12.
Stewart WF, Ricci JA, Chee E, Hahn SR, Morganstein D. Cost of lost productive work time among US workers with depression. JAMA. 2003;289:3135–44.
Bleich A, Solomon Z. Evaluation of psychiatric disability in PTSD of military origin. Isr J Psychiatry Relat Sci. 2004;41:268–76.
McNally RJ, Frueh BC. Why are Iraq and Afghanistan War veterans seeking PTSD disability compensation at unprecedented rates? J Anxiety Disord. 2013;27:520–6.
Surís A, Lind L. Military sexual trauma: a review of prevalence and associated health consequences in veterans. Trauma Violence Abuse. 2008;9:250–69.
Lew HL, Otis JD, Tun C, Kerns RD, Clark ME, Cifu DX. Prevalence of chronic pain, posttraumatic stress disorder, and persistent postconcussive symptoms in OIF/OEF veterans: polytrauma clinical triad. J Rehabil Res Dev. 2009;46:697–702.
Haskell SG, Gordon KS, Mattocks K, Duggal M, Erdos J, Justice A, et al. Gender differences in rates of depression, PTSD, pain, obesity, and military sexual trauma among Connecticut war veterans of Iraq and Afghanistan. J Womens Health. 2010;19:267–71.
Stiell IG, Clement CM, McKnight RD, Brison R, Schull MJ, Rowe BH, et al. The Canadian C-spine rule versus the NEXUS low-risk criteria in patients with trauma. N Engl J Med. 2003;349:2510–8.
Régnier M-A, Raux M, Le Manach Y, Asencio Y, Gaillard J, Devilliers C, et al. Prognostic significance of blood lactate and lactate clearance in trauma patients. Anesthesiology. 2012;117:1276–88.
Guyette F, Suffoletto B, Castillo J-L, Quintero J, Callaway C, Puyana J-C. Prehospital serum lactate as a predictor of outcomes in trauma patients: a retrospective observational study. J Trauma Acute Care Surg. 2011;70:782–6.
Abramson D, Scalea TM, Hitchcock R, Trooskin SZ, Henry SM, Greenspan J. Lactate clearance and survival following injury. J Trauma. 1993;35:584–8. discussion 588–589
Rutherford EJ, Morris JJ, Reed GW, Hall KS. Base deficit stratifies mortality and determines therapy. J Trauma. 1992;33:417–23.
Kincaid EH, Miller PR, Meredith JW, Rahman N, Chang MC. Elevated arterial base deficit in trauma patients: a marker of impaired oxygen utilization. J Am Coll Surg. 1998;187:384–92.
Kearns MC, Ressler KJ, Zatzick D, Rothbaum BO. Early interventions for PTSD: a review. Depression Anxiety. 2012;29:833–42.
Shalev AY, Ankri Y, Gilad M, Israeli-Shalev Y, Adessky R, Qian M, et al. Long-term outcome of early interventions to prevent posttraumatic stress disorder. J Clin Psychiatry. 2016;77:e580–7.
Fritz JM, Magel JS, McFadden M, Asche C, Thackeray A, Meier W, et al. Early physical therapy vs usual care in patients with recent-onset low back pain: a randomized clinical trial. JAMA. 2015;314:1459–67.
Litz BT, Gray MJ, Bryant RA, Adler AB. Early intervention for trauma: current status and future directions. Clin Psychol Sci Pract. 2002;9:112–134.
Kleim B, Ehlers A, Glucksman E. Early predictors of chronic post-traumatic stress disorder in assault survivors. Psychol Med. 2007;37:1457–67.
Kessler RC, Rose S, Koenen KC, Karam EG, Stang PE, Stein DJ, et al. How well can post‐traumatic stress disorder be predicted from pre‐trauma risk factors? An exploratory study in the WHO World Mental Health Surveys. World Psychiatry. 2014;13:265–74.
Karstoft K-I, Statnikov A, Andersen SB, Madsen T, Galatzer-Levy IR. Early identification of posttraumatic stress following military deployment: application of machine learning methods to a prospective study of Danish soldiers. J Affect Disord. 2015;184:170–5.
Powers MB, Warren AM, Rosenfield D, Roden-Foreman K, Bennett M, Reynolds MC, et al. Predictors of PTSD symptoms in adults admitted to a Level I trauma center: a prospective analysis. J Anxiety Disord. 2014;28:301–9.
Symes L, Maddoux J, McFarlane J, Pennings J. A risk assessment tool to predict sustained PTSD symptoms among women reporting abuse. J Women's Health. 2016;25:340–7.
Rosellini AJ, Dussaillant F, Zubizarreta JR, Kessler RC, Rose S. Predicting posttraumatic stress disorder following a natural disaster. J Psychiatr Res. 2018;96:15–22.
Galatzer-Levy IR, Karstoft KI, Statnikov A, Shalev AY. Quantitative forecasting of PTSD from early trauma responses: a Machine Learning application. J Psychiatr Res. 2014;59:68–76.
Karstoft KI, Galatzer-Levy IR, Statnikov A, Li Z, Shalev AY. Bridging a translational gap: using machine learning to improve the prediction of PTSD. BMC Psychiatry. 2015;15:30.
Shalev AY, Gevonden M, Ratanatharathorn A, Laska E, van der Mei WF, Qi W, et al. Estimating the risk of PTSD in recent trauma survivors: results of the International Consortium to Predict PTSD (ICPP). World Psychiatry. 2019;18:77–87.
Freedman SA, Brandes D, Peri T, Shalev A. Predictors of chronic post-traumatic stress disorder. A prospective study. Br J Psychiatry. 1999;174:353–9.
Gatchel RJ, Peng YB, Peters ML, Fuchs PN, Turk DC. The biopsychosocial approach to chronic pain: scientific advances and future directions. Psychological Bull. 2007;133:581.
Yehuda R. Biology of posttraumatic stress disorder. J Clin Psychiatry. 2001;62(Suppl 17):41–6. Review.
Krishnan V, Nestler EJ. Linking molecules to mood: new insight into the biology of depression. Am J Psychiatry. 2010;167:1305–20.
Michopoulos V, Beurel E, Gould F, Dhabhar FS, Schultebraucks K, Galatzer-Levy I, et al. Association of prospective risk for chronic PTSD symptoms with low TNFalpha and IFNgamma concentrations in the immediate aftermath of trauma exposure. Am J Psychiatry. 2019;29:appiajp201919010039.
Stevens JS, Kim YJ, Galatzer-Levy IR, Reddy R, Ely TD, Nemeroff CB, et al. Amygdala reactivity and anterior cingulate habituation predict posttraumatic stress disorder symptom maintenance after acute civilian trauma. Biol Psychiatry. 2017;81:1023–9.
Galatzer-Levy IR, Ma S, Statnikov A, Yehuda R, Shalev AY. Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD. Transl Psychiatry. 2017;7:e0.
Hinrichs R, van Rooij SJ, Michopoulos V, Schultebraucks K, Winters S, Maples-Keller J, et al. Increased skin conductance response in the immediate aftermath of trauma predicts PTSD risk. Chronic Stress. 2019;3. https://doi.org/10.1177/2470547019844441. Epub 24 Apr 2019.
Bonne O, Brandes D, Gilboa A, Gomori JM, Shenton ME, Pitman RK, et al. Longitudinal MRI study of hippocampal volume in trauma survivors with PTSD. Am J Psychiatry. 2001;158:1248–51.
Shalev AY, Sahar T, Freedman S, Peri T, Glick N, Brandes D, et al. A prospective study of heart rate response following trauma and the subsequent development of posttraumatic stress disorder. Arch Gen Psychiatry. 1998;55:553–9.
Yehuda R, McFarlane AC, Shalev AY. Predicting the development of posttraumatic stress disorder from the acute response to a traumatic event. Biol Psychiatry. 1998;44:1305–13.
Shalev AY, Peri T, Brandes D, Freedman S, Orr SP, Pitman RK. Auditory startle response in trauma survivors with posttraumatic stress disorder: a prospective study. Am J Psychiatry. 2000;157:255–61.
Group BDW, Atkinson AJ Jr, Colburn WA, DeGruttola VG, DeMets DL, Downing GJ, et al. Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther. 2001;69:89–95.
Michopoulos V, Norrholm SD, Jovanovic T. Diagnostic biomarkers for posttraumatic stress disorder: promising horizons from translational neuroscience research. Biol Psychiatry. 2015;78:344–53.
Tracey I, Woolf CJ, Andrews NA. Composite pain biomarker signatures for objective assessment and effective treatment. Neuron. 2019;101:783–800.
Strawbridge R, Young AH, Cleare AJ. Biomarkers for depression: recent insights, current challenges and future prospects. Neuropsychiatr Dis Treat. 2017;13:1245.
Domingo-Fernandez D, Provost A, Marin-Llao J, Lasseter H, Diaz K, Daskalakis N, et al. PTSD biomarker database: deep dive meta-database for PTSD biomarkers, visualizations, and analysis tools. 547901. https://doi.org/10.1093/database/baz081.
Blacker CJ, Frye MA, Morava-Kozicz E, Kozicz T, Veldic M. A review of epigenetics of PTSD in comorbid psychiatric conditions. Genes. 2019;10:140.
Sharma S, Ressler KJ. Genomic updates in understanding PTSD. Prog Neuropsychopharmacol Biol Psychiatry. 2018;90:197–203.
Nievergelt CM, Ashley-Koch AE, Dalvie S, Hauser MA, Morey RA, Smith AK, et al. Genomic approaches to posttraumatic stress disorder: the psychiatric genomic consortium initiative. Biol Psychiatry. 2018;83:831–9.
Diatchenko L, Nackley AG, Tchivileva IE, Shabalina SA, Maixner W. Genetic architecture of human pain perception. Trends Genet. 2007;23:605–13.
Young EE, Lariviere WR, Belfer I. Genetic basis of pain variability: recent advances. J Med Genet. 2012;49:1–9.
Descalzi G, Ikegami D, Ushijima T, Nestler EJ, Zachariou V, Narita M. Epigenetic mechanisms of chronic pain. Trends Neurosci. 2015;38:237–46.
Nestler EJ. Epigenetic mechanisms of depression. JAMA Psychiatry. 2014;71:454–6.
Sullivan PF, Neale MC, Kendler KS. Genetic epidemiology of major depression: review and meta-analysis. Am J Psychiatry. 2000;157:1552–62.
Zannas AS, Provençal N, Binder EB. Epigenetics of posttraumatic stress disorder: current evidence, challenges, and future directions. Biol Psychiatry. 2015;78:327–35.
Zannas AS, Binder, E, Mehta, D. (2016). Bremner JD, (ed). Genomics of PTSD. In Posttraumatic Stress Disorder. https://doi.org/10.1002/9781118356142.ch10.
Segman RH, Shefi N, Goltser-Dubner T, Friedman N, Kaminski N, Shalev AY. Peripheral blood mononuclear cell gene expression profiles identify emergent post-traumatic stress disorder among trauma survivors. Mol Psychiatry. 2005;10:500–13. 425
van Zuiden M, Geuze E, Willemen HL, Vermetten E, Maas M, Heijnen CJ, et al. Pre-existing high glucocorticoid receptor number predicting development of posttraumatic stress symptoms after military deployment. Am J Psychiatry. 2011;168:89–96.
van Zuiden M, Geuze E, Willemen HL, Vermetten E, Maas M, Amarouchi K, et al. Glucocorticoid receptor pathway components predict posttraumatic stress disorder symptom development: a prospective study. Biol Psychiatry. 2012;71:309–16.
Glatt SJ, Tylee DS, Chandler SD, Pazol J, Nievergelt CM, Woelk CH, et al. Blood-based gene-expression predictors of PTSD risk and resilience among deployed marines: a pilot study. Am J Med Genet B Neuropsychiatr Genet. 2013;162b:313–26.
van Zuiden M, Kavelaars A, Vermetten E, Olff M, Geuze E, Heijnen C. Pre-deployment differences in glucocorticoid sensitivity of leukocytes in soldiers developing symptoms of PTSD, depression or fatigue persist after return from military deployment. Psychoneuroendocrinology. 2015;51:513–24.
Reijnen A, Geuze E, Vermetten E. Individual variation in plasma oxytocin and vasopressin levels in relation to the development of combat-related PTSD in a large military cohort. J Psychiatr Res. 2017;94:88–95.
Walsh K, Nugent NR, Kotte A, Amstadter AB, Wang S, Guille C, et al. Cortisol at the emergency room rape visit as a predictor of PTSD and depression symptoms over time. Psychoneuroendocrinology. 2013;38:2520–8.
van Zuiden M, Heijnen CJ, Maas M, Amarouchi K, Vermetten E, Geuze E, et al. Glucocorticoid sensitivity of leukocytes predicts PTSD, depressive and fatigue symptoms after military deployment: a prospective study. Psychoneuroendocrinology. 2012;37:1822–36.
Yu S, Chen C, Pan Y, Kurz MC, Datner E, Hendry PL, et al. Genes known to escape X chromosome inactivation predict co-morbid chronic musculoskeletal pain and posttraumatic stress symptom development in women following trauma exposure. Am J Med Genet B Neuropsychiatr Genet. 2018;180:415–27.
Vaiva G, Boss V, Ducrocq F, Fontaine M, Devos P, Brunet A, et al. Relationship between posttrauma GABA plasma levels and PTSD at 1-year follow-up. Am J Psychiatry. 2006;163:1446–8.
Reijnen A, Geuze E, Eekhout I, Maihofer AX, Nievergelt CM, Baker DG, et al. Biological profiling of plasma neuropeptide Y in relation to posttraumatic stress symptoms in two combat cohorts. Biol Psychol. 2018;134:72–9.
Cohen M, Meir T, Klein E, Volpin G, Assaf M, Pollack S. Cytokine levels as potential biomarkers for predicting the development of posttraumatic stress symptoms in casualties of accidents. Int J Psychiatry Med. 2011;42:117–31.
Inslicht SS, Otte C, McCaslin SE, Apfel BA, Henn-Haase C, Metzler T, et al. Cortisol awakening response prospectively predicts peritraumatic and acute stress reactions in police officers. Biol Psychiatry. 2011;70:1055–62.
Eraly SA, Nievergelt CM, Maihofer AX, Barkauskas DA, Biswas N, Agorastos A, et al. Assessment of plasma C-reactive protein as a biomarker of posttraumatic stress disorder risk. JAMA Psychiatry. 2014;71:423–31.
Vaiva G, Thomas P, Ducrocq F, Fontaine M, Boss V, Devos P, et al. Low posttrauma GABA plasma levels as a predictive factor in the development of acute posttraumatic stress disorder. Biol Psychiatry. 2004;55:250–4.
Linnstaedt SD, Walker MG, Parker JS, Yeh E, Sons RL, Zimny E, et al. MicroRNA circulating in the early aftermath of motor vehicle collision predict persistent pain development and suggest a role for microRNA in sex-specific pain differences. Mol Pain. 2015;11:66.
Rushton AB, Evans DW, Middlebrook N, Heneghan NR, Small C, Lord J, et al. Development of a screening tool to predict the risk of chronic pain and disability following musculoskeletal trauma: protocol for a prospective observational study in the United Kingdom. BMJ open. 2018;8:e017876.
Linnstaedt SD, Rueckeis CA, Riker KD, Pan Y, Wu A, Yu S, et al. microRNA-19b predicts widespread pain and posttraumatic stress symptom risk in a sex-dependent manner following trauma exposure. Pain. 2019. https://doi.org/10.1097/j.pain.0000000000001709. [Epub ahead of print].
Mauck MC, Linnstaedt SD, Bortsov A, Kurz M, Hendry PL, Lewandowski C, et al. Vitamin D insufficiency increases risk of chronic pain among African Americans experiencing motor vehicle collision. Pain. 2019. https://doi.org/10.1097/j.pain.0000000000001728. [Epub ahead of print].
Chrousos GP, Gold PW. The concepts of stress and stress system disorders. Overview of physical and behavioral homeostasis. JAMA. 1992;267:1244–52.
Gassen NC, Chrousos GP, Binder EB, Zannas AS. Life stress, glucocorticoid signaling, and the aging epigenome: implications for aging-related diseases. Neurosci Biobehav Rev. 2016;74:356–65.
Breen MS, Maihofer AX, Glatt SJ, Tylee DS, Chandler SD, Tsuang MT, et al. Gene networks specific for innate immunity define post-traumatic stress disorder. Mol Psychiatry. 2015;20:1538–45.
Pervanidou P, Kolaitis G, Charitaki S, Margeli A, Ferentinos S, Bakoula C, et al. Elevated morning serum interleukin (IL)-6 or evening salivary cortisol concentrations predict posttraumatic stress disorder in children and adolescents six months after a motor vehicle accident. Psychoneuroendocrinology. 2007;32:991–9.
Gandubert C, Scali J, Ancelin ML, Carriere I, Dupuy AM, Bagnolini G, et al. Biological and psychological predictors of posttraumatic stress disorder onset and chronicity. A one-year prospective study. Neurobiol Stress. 2016;3:61–7.
Andrews JA, Neises KD. Cells, biomarkers, and post‐traumatic stress disorder: evidence for peripheral involvement in a central disease. J Neurochem. 2012;120:26–36.
Harris LW, Pietsch S, Cheng TM, Schwarz E, Guest PC, Bahn S. Comparison of peripheral and central schizophrenia biomarker profiles. PloS ONE. 2012;7:e46368.
Milivojevic V, Sinha R. Central and peripheral biomarkers of stress response for addiction risk and relapse vulnerability. Trends Mol Med. 2018;24:173–86.
L Kelso M, H Oestreich J. Traumatic brain injury: central and peripheral role of α7 nicotinic acetylcholine receptors. Curr Drug Targets. 2012;13:631–6.
Yubero-Lahoz S, Robledo P, Farré M, de La, Torre R. Platelet SERT as a peripheral biomarker of serotonergic neurotransmission in the central nervous system. Curr Med Chem. 2013;20:1382–96.
Sullivan PF, Fan C, Perou CM. Evaluating the comparability of gene expression in blood and brain. Am J Med Genet B Neuropsychiatr Genet. 2006;141:261–8.
Simeoli R, Montague K, Jones HR, Castaldi L, Chambers D, Kelleher JH, et al. Exosomal cargo including microRNA regulates sensory neuron to macrophage communication after nerve trauma. Nat Commun. 2017;8:1778.
Mustapic M, Eitan E, Werner JK Jr, Berkowitz ST, Lazaropoulos MP, Tran J, et al. Plasma extracellular vesicles enriched for neuronal origin: a potential window into brain pathologic processes. Front Neurosci. 2017;11:278.
Rifai N, Gillette MA, Carr SA. Protein biomarker discovery and validation: the long and uncertain path to clinical utility. Nat Biotechnol. 2006;24:971.
Kristman V, Manno M, Côté P. Loss to follow-up in cohort studies: how much is too much? Eur J Epidemiol. 2004;19:751–60.
Gorelick MH. Bias arising from missing data in predictive models. J Clin Epidemiol. 2006;59:1115–23.
Kleinbaum DG, Morgenstern H, Kupper LL. Selection bias in epidemiologic studies. Am J Epidemiol. 1981;113:452–63.
Tripepi G, Jager K, Dekker F, Wanner C, Zoccali C. Bias in clinical research. Kidney Int. 2008;73:148–53.
Coughlin SS. Recall bias in epidemiologic studies. J Clin Epidemiol. 1990;43:87–91.
Fisher RJ. Social desirability bias and the validity of indirect questioning. J Consum Res. 1993;20:303–15.
Miettinen OS, Cook EF. Confounding: essence and detection. Am J Epidemiol. 1981;114:593–603.
Cheng HH, Yi HS, Kim Y, Kroh EM, Chien JW, Eaton KD, et al. Plasma processing conditions substantially influence circulating microRNA biomarker levels. PloS ONE. 2013;8:e64795.
Kirschner MB, Kao SC, Edelman JJ, Armstrong NJ, Vallely MP, van Zandwijk N, et al. Haemolysis during sample preparation alters microRNA content of plasma. PloS ONE. 2011;6:e24145.
Pai JK, Curhan GC, Cannuscio CC, Rifai N, Ridker PM, Rimm EB. Stability of novel plasma markers associated with cardiovascular disease: processing within 36h of specimen collection. Clin Chem. 2002;48:1781–4.
Pischon T, Hotamisligil GS, Rimm EB. Adiponectin: stability in plasma over 36h and within-person variation over 1 year. Clin Chem. 2003;49:650–2.
Kim D-J, Linnstaedt S, Palma J, Park JC, Ntrivalas E, Kwak-Kim JY, et al. Plasma components affect accuracy of circulating cancer-related microRNA quantitation. J Mol Diagn. 2012;14:71–80.
Mraz M, Malinova K, Mayer J, Pospisilova S. MicroRNA isolation and stability in stored RNA samples. Biochem Biophys Res Commun. 2009;390:1–4.
Podolska A, Kaczkowski B, Litman T, Fredholm M, Cirera S. How the RNA isolation method can affect microRNA microarray results. Acta Biochim Pol. 2011;58:535–40.
Yeung D, Ciotti S, Purushothama S, Gharakhani E, Kuesters G, Schlain B, et al. Evaluation of highly sensitive immunoassay technologies for quantitative measurements of sub-pg/mL levels of cytokines in human serum. J Immunol Methods. 2016;437:53–63.
Polaskova V, Kapur A, Khan A, Molloy MP, Baker MS. High-abundance protein depletion: comparison of methods for human plasma biomarker discovery. Electrophoresis. 2010;31:471–82.
Percy AJ, Chambers AG, Yang J, Domanski D, Borchers CH. Comparison of standard- and nano-flow liquid chromatography platforms for MRM-based quantitation of putative plasma biomarker proteins. Anal Bioanal Chem. 2012;404:1089–101.
Kroot JJ, Kemna EH, Bansal SS, Busbridge M, Campostrini N, Girelli D, et al. Results of the first international round robin for the quantification of urinary and plasma hepcidin assays: need for standardization. Haematologica. 2009;94:1748–52.
Dorgan JF, Fears TR, McMahon RP, Aronson Friedman L, Patterson BH, Greenhut SF. Measurement of steroid sex hormones in serum: a comparison of radioimmunoassay and mass spectrometry. Steroids. 2002;67:151–8.
Pritchard CC, Cheng HH, Tewari M. MicroRNA profiling: approaches and considerations. Nat Rev Genet. 2012;13:358.
Tian G, Yin X, Luo H, Xu X, Bolund L, Zhang X. Sequencing bias: comparison of different protocols of microRNA library construction. BMC Biotechnol. 2010;10:64.
Hafner M, Renwick N, Brown M, Mihailović A, Holoch D, Lin C, et al. RNA-ligase-dependent biases in miRNA representation in deep-sequenced small RNA cDNA libraries. RNA. 2011;17:1697–712.
Baran-Gale J, Kurtz CL, Erdos MR, Sison C, Young A, Fannin EE, et al. Addressing bias in small RNA library preparation for sequencing: a new protocol recovers microRNAs that evade capture by current methods. Front Genet. 2015;6:352.
Sorefan K, Pais H, Hall AE, Kozomara A, Griffiths-Jones S, Moulton V, et al. Reducing ligation bias of small RNAs in libraries for next generation sequencing. Silence. 2012;3:4.
Orton DJ, Doucette AA. Proteomic workflows for biomarker identification using mass spectrometry—technical and statistical considerations during initial discovery. Proteomes. 2013;1:109–27.
Bullard JH, Purdom E, Hansen KD, Dudoit S. Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments. BMC Bioinform. 2010;11:94.
Feng Z, Prentice R, Srivastava S. Research issues and strategies for genomic and proteomic biomarker discovery and validation: a statistical perspective. Pharmacogenomics. 2004;5:709–19.
Ray P, Le Manach Y, Riou B, Houle TT. Statistical evaluation of a biomarker. Anesthesiology. 2010;112:1023–40.
McDermott JE, Wang J, Mitchell H, Webb-Robertson BJ, Hafen R, Ramey J, et al. Challenges in biomarker discovery: combining expert insights with statistical analysis of complex omics data. Expert Opin Med Diagn. 2013;7:37–51.
Nielsen TO, Parker JS, Leung S, Voduc D, Ebbert M, Vickery T, et al. A comparison of PAM50 intrinsic subtyping with immunohistochemistry and clinical prognostic factors in tamoxifen-treated estrogen receptor–positive breast cancer. Clin Cancer Res. 2010;16:5222–32.
Heilmeier U, Hackl M, Skalicky S, Weilner S, Schroeder F, Vierlinger K, et al. Serum miRNA signatures are indicative of skeletal fractures in postmenopausal women with and without type 2 diabetes and influence osteogenic and adipogenic differentiation of adipose tissue-derived mesenchymal stem cells in vitro. J Bone Miner Res. 2016;31:2173–92.
Hackl M, Heilmeier U, Weilner S, Grillari J. Circulating microRNAs as novel biomarkers for bone diseases—complex signatures for multifactorial diseases? Mol Cell Endocrinol. 2016;432:83–95.
Kocijan R, Muschitz C, Geiger E, Skalicky S, Baierl A, Dormann R, et al. Circulating microRNA signatures in patients with idiopathic and postmenopausal osteoporosis and fragility fractures. J Clin Endocrinol Metab. 2016;101:4125–34.
Bazarian JJ, Biberthaler P, Welch RD, Lewis LM, Barzo P, Bogner-Flatz V, et al. Serum GFAP and UCH-L1 for prediction of absence of intracranial injuries on head CT (ALERT-TBI): a multicentre observational study. Lancet Neurol. 2018;17:782–9.
Wilson PW, D’Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB. Prediction of coronary heart disease using risk factor categories. Circulation. 1998;97:1837–47.
Tzoulaki I, Liberopoulos G, Ioannidis JP. Assessment of claims of improved prediction beyond the Framingham risk score. JAMA. 2009;302:2345–52.
Ridker PM, Buring JE, Rifai N, Cook NR. Development and validation of improved algorithms for the assessment of global cardiovascular risk in women: the Reynolds Risk Score. Jama. 2007;297:611–9.
Kline JA, Mitchell AM, Kabrhel C, Richman PB, Courtney DM. Clinical criteria to prevent unnecessary diagnostic testing in emergency department patients with suspected pulmonary embolism. J Thromb Haemost. 2004;2:1247–55.
Kline JA, Courtney DM, Kabrhel C, Moore CL, Smithline HA, Plewa MC, et al. Prospective multicenter evaluation of the pulmonary embolism rule-out criteria. J Thromb Haemost. 2008;6:772–80.
FDA Critical Path Opportunities Report. Innovation or stagnation: challenge and opportunity on the critical path to new medical products. 2004. http://wayback.archive-it.org/7993/20180125032208/https://www.fda.gov/ScienceResearch/SpecialTopics/CriticalPathInitiative/CriticalPathOpportunitiesReports/ucm077262.htm#execsummary.
C-Path. Critical path Institute encouraged by FDA to move forward on Type 1 Diabetes biomarker initiative. 2018. https://c-path.org/critical-path-institute-encouraged-by-fda-to-move-forward-on-type-1-diabetes-biomarker-initiative/.
Galatzer-Levy IR, Bryant RA. 636,120 ways to have posttraumatic stress disorder. Perspect Psychol Sci. 2013;8:651–62.
Flint J, Munafo M. Schizophrenia: genesis of a complex disease. Nature. 2014;511:412–3.
Wray NR, Ripke S, Mattheisen M, Trzaskowski M, Byrne EM, Abdellaoui A, et al. Genome-wide association analyses identify 44 risk variants and refine the genetic architecture of major depression. Nat Genet. 2018;50:668–81.
Schizophrenia Working Group of the Psychiatric Genomics Consortium. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421–7.
Siekmeier PJ. An in silico, biomarker-based method for the evaluation of virtual neuropsychiatric drug effects. Neural Comput. 2017;29:1021–52.
Lin Y, Qian F, Shen L, Chen F, Chen J, Shen B. Computer-aided biomarker discovery for precision medicine: data resources, models and applications. Brief Bioinform. 2017;20:952–75.
Bhake RC, Leendertz JA, Linthorst AC, Lightman SL. Automated 24-hours sampling of subcutaneous tissue free cortisol in humans. J Med Eng Technol. 2013;37:180–4.
Wust S, Federenko IS, van Rossum EF, Koper JW, Hellhammer DH. Habituation of cortisol responses to repeated psychosocial stress-further characterization and impact of genetic factors. Psychoneuroendocrinology. 2005;30:199–11.
Galatzer-Levy I, Ma S, Statnikov A, Yehuda R, Shalev A. Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD. Transl Psychiatry. 2017;7:e1070.
Xia J, Broadhurst DI, Wilson M, Wishart DS. Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics. 2013;9:280–99.
Kulkarni MM. Digital multiplexed gene expression analysis using the NanoString nCounter system. Curr Protoc Mol Biol. 2011;25:Unit25B.10.
Eastel JM, Lam KW, Lee NL, Lok WY, Tsang AHF, Pei XM, et al. Application of NanoString technologies in companion diagnostic development. Expert Rev Mol Diagn. 2019;19:591–8.
Simon R. Sensitivity, specificity, PPV, and NPV for predictive biomarkers. JNCI. 2015;107:djv153
Pepe MS, Janes H, Li CI, Bossuyt PM, Feng Z, Hilden J. Early-phase studies of biomarkers: what target sensitivity and specificity values might confer clinical utility? Clin Chem. 2016;62:737–42.
Boateng D, Agyemang C, Beune E, Meeks K, Smeeth L, Schulze MB, et al. Cardiovascular disease risk prediction in sub-Saharan African populations—comparative analysis of risk algorithms in the RODAM study. Int J Cardiol. 2018;254:310–5.
Vigo D, Thornicroft G, Atun R. Estimating the true global burden of mental illness. Lancet Psychiatry. 2016;3:171–8.
McGeary D, Moore M, Vriend CA, Peterson AL, Gatchel RJ. The evaluation and treatment of comorbid pain and PTSD in a military setting: an overview. J Clin Psychol Med Settings. 2011;18:155.
Feuerstein M, Berkowitz SM, Peck CA Jr. Musculoskeletal-related disability in US Army personnel: prevalence, gender, and military occupational specialties. J Occup Environ Med. 1997;39:68–78.
Acknowledgements
We acknowledge Elizabeth Moreton and Michelle Cawley from the University of North Carolina Health Sciences Library for support in performing the literature search for this review article. SDL is supported by NIH K01AR071504, SAM is supported by NIH R01AR064700 and U01MH110925, KCK is supported by NIH R01MH106595, R01MH101269, U01MH110925, and T32MH017119, KJR is supported by NIH U01MH110925, R01MH071537, R01MH094757, and R01MH106595.
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KJR provides fee-for-service consultation for Johnson & Johnson, Verily, and Alkermes. He has received sponsored research unrelated to this work from Brainsway and Takeda. He also holds patents for a number of targets related to improving extinction of fear, however, he has received no equity or income within the last 3 years related to these. He receives or has received research funding from NIMH, NIAAA, HHMI, NARSAD, and the Burroughs Wellcome Foundation. The other authors declare no conflicts of interest.
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Linnstaedt, S.D., Zannas, A.S., McLean, S.A. et al. Literature review and methodological considerations for understanding circulating risk biomarkers following trauma exposure. Mol Psychiatry 25, 1986–1999 (2020). https://doi.org/10.1038/s41380-019-0636-5
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DOI: https://doi.org/10.1038/s41380-019-0636-5
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